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Over the past few decades, the world has seen substantial tropical cyclone (TC) damages, with the 2017 Hurricanes Harvey, Irma and Maria entering the top-5 costliest Atlantic hurricanes ever. Calculating TC risk at a global scale, however, has proven difficult given the limited temporal and spatial information on TCs across much of the global coastline. Here, we present a novel database on TC characteristics on a global scale using a newly developed synthetic resampling algorithm we call STORM (Synthetic Tropical cyclOne geneRation Model). STORM can be applied to any meteorological dataset to statistically resample and model TC tracks and intensities. We apply STORM to extracted TCs from 38 years of historical data from IBTrACS to statistically extend this dataset to 10,000 years of TC activity. We show that STORM preserves the TC statistics as found in the original dataset. The STORM dataset can be used for TC hazard assessments and risk modeling in TC-prone regions.Measurement(s)cycloneTechnology Type(s)computational modeling techniqueFactor Type(s)year • basinSample Characteristic - Environmentclimate systemSample Characteristic - LocationEarth (planet)Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11733585

This study examines the implications of recent advances in global climate modelling for simulating storm surges. Following the ERA-Interim (0.75° × 0.75°) global climate reanalysis, in 2018 the European Centre for Medium-range Weather Forecasts released its successor, the ERA5 (0.25° × 0.25°) reanalysis. Using the Global Tide and Surge Model, we analyse eight historical storm surge events driven by tropical—and extra-tropical cyclones. For these events we extract wind fields from the two reanalysis datasets and compare these against satellite-based wind field observations from the Advanced SCATterometer. The root mean squared errors in tropical cyclone wind speed reduce by 58% in ERA5, compared to ERA-Interim, indicating that the mean sea-level pressure and corresponding strong 10-m winds in tropical cyclones greatly improved from ERA-Interim to ERA5. For four of the eight historical events we validate the modelled storm surge heights with tide gauge observations. For Hurricane Irma, the modelled surge height increases from 0.88 m with ERA-Interim to 2.68 m with ERA5, compared to an observed surge height of 2.64 m. We also examine how future advances in climate modelling can potentially further improve global storm surge modelling by comparing the results for ERA-Interim and ERA5 against the operational Integrated Forecasting System (0.125° × 0.125°). We find that a further increase in model resolution results in a better representation of the wind fields and associated storm surges, especially for small size tropical cyclones. Overall, our results show that recent advances in global climate modelling have the potential to increase the accuracy of early-warning systems and coastal flood hazard assessments at the global scale.

Since 1990, natural hazards have led to over 1.6 million fatalities globally, and economic losses are estimated at an average of around USD 260–310 billion per year. The scientific and policy communities recognise the need to reduce these risks. As a result, the last decade has seen a rapid development of global models for assessing risk from natural hazards at the global scale. In this paper, we review the scientific literature on natural hazard risk assessments at the global scale, and we specifically examine whether and how they have examined future projections of hazard, exposure, and/or vulnerability. In doing so, we examine similarities and differences between the approaches taken across the different hazards, and we identify potential ways in which different hazard communities can learn from each other. For example, there are a number of global risk studies focusing on hydrological, climatological, and meteorological hazards that have included future projections and disaster risk reduction measures (in the case of floods), whereas fewer exist in the peer-reviewed literature for global studies related to geological hazards. On the other hand, studies of earthquake and tsunami risk are now using stochastic modelling approaches to allow for a fully probabilistic assessment of risk, which could benefit the modelling of risk from other hazards. Finally, we discuss opportunities for learning from methods and approaches being developed and applied to assess natural hazard risks at more continental or regional scales. Through this paper, we hope to encourage further dialogue on knowledge sharing between disciplines and communities working on different hazards and risk and at different spatial scales.

Tropical cyclones (TCs) cause devastating damage to life and property. Historical TC data is scarce, complicating adequate TC risk assessments. Synthetic TC models are specifically designed to overcome this scarcity. While these models have been evaluated on their ability to simulate TC activity, no study to date has focused on model performance and applicability in TC risk assessments. This study performs the intercomparison of four different global-scale synthetic TC datasets in the impact space, comparing impact return period curves, probability of rare events, and hazard intensity distribution over land. We find that the model choice influences the costliest events, particularly in basins with limited TC activity. Modelled direct economic damages in the North Indian Ocean, for instance, range from 40 to 246 billion USD for the 100-yr event over the four hazard sets. We furthermore provide guidelines for the suitability of the different synthetic models for various research purposes. Various synthetic tropical cyclone datasets exist for risk assessment purposes. Here, the authors conduct a global dataset comparison to assess their suitability and applicability in answering different impact-related questions.

Storm surges that occur along low-lying, densely populated coastlines can leave devastating societal, economical, and ecological impacts. To protect coastal communities from flooding, return periods of storm tides, defined as the combination of the surge and tide, must be accurately evaluated. Here we present storm tide return periods using a novel integration of two modelling techniques. For surges induced by extratropical cyclones, we use a 38-year time series based on the ERA5 climate reanalysis. For surges induced by tropical cyclones, we use synthetic tropical cyclones from the STORM dataset representing 10,000 years under current climate conditions. Tropical and extratropical cyclone surge levels are probabilistically combined with tidal levels, and return periods are computed empirically. We estimate that 78 million people are exposed to a 1 in 1000-year flood caused by extratropical cyclones, which more than doubles to 192 M people when taking tropical cyclones into account. Our results show that previous studies have underestimated the global exposure to low-probability coastal flooding by 31%.

We assess the suitability of ECMWF Integrated Forecasting System (IFS) data for the global modeling of tropical cyclone (TC) storm surges. We extract meteorological forcing from the IFS at a 0.225° horizontal resolution for eight historical TCs and simulate the corresponding surges using the global tide and surge model. Maximum surge heights for Hurricanes Irma and Sandy are compared with tide gauge observations, with R2-values of 0.86 and 0.74 respectively. Maximum surge heights for the other TCs are in line with literature. Our case studies demonstrate that a horizontal resolution of 0.225° is sufficient for the large-scale modeling of TC surges. By upscaling the meteorological forcing to coarser resolutions as low as 1.0°, we assess the effects of horizontal resolution on the performance of surge modeling. We demonstrate that coarser resolutions result in lower-modeled surges for all case studies, with modeled surges up to 1 m lower for Irma and Nargis. The largest differences in surges between the different resolutions are found for the TCs with the highest surges. We discuss possible drivers of maximum surge heights (TC size, intensity, and coastal slope and complexity), and find that coastal complexity and slope play a more profound role than TC size and intensity alone. The highest surges are found in areas with complex coastlines (fractal dimension > 1.10) and, in general, shallow coastlines. Our findings show that using high-resolution meteorological forcing is particularly beneficial for areas prone to high TC surges, since these surges are reduced the most in coarse-resolution datasets.

During a hurricane, it is vital that individuals receive communications that are easy to process and provide sufficient information to allow informed hurricane preparedness decisions and prevent loss of life. We study how different hurricane warning scales, the traditional Saffir-Simpson Hurricane Wind Scale (SSHWS) versus the newly developed Tropical Cyclone Severity Scale (TCSS), impact intent to evacuate and understanding of hurricane severity. We use a between-subject design where participants are assigned to either the traditional SSHWS or the new TCSS scale. We collected data in a large-scale (~ 4000 participants) online experiment to examine potential differences in comprehension, risk perception, anticipated evacuation, and preparation decisions among residents in U.S. coastal states prone to hurricanes. We find that participants using the TCSS scale are better at identifying the main hazard of a hurricane. For evacuation, the TCSS leads to significantly higher evacuation intent as opposed to SSHWS in cases where the TCSS is at least two categories higher (due to rainfall or storm surge being the main hazard rather than wind). In addition, the TCSS also seems to have a positive effect on taking appropriate precautionary measures, though not always at our stated significance level. Overall, our results demonstrate that people make better informed and more appropriate decisions with the TCSS as opposed to the currently used SSHWS. Protocol Registration The stage 1 protocol for this Registered Report was accepted in principle on 14 October 2024. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/AYXTK . The approved Stage 1 protocol is available here: https://osf.io/m3swr .

The Caribbean region is prone to the strong winds and low air pressures of tropical cyclones and their corresponding storm surge that driving coastal flooding. To protect coastal communities from the impacts of tropical cyclones, it is important to understand how this impact of tropical cyclones might change towards the future. This study applies the storyline approach to show what tropical cyclones Maria (2017) and Dorian (2019) could look like in a 2 °C and 3.4 °C warmer future climate. These two possible future climates are simulated with a high-resolution regional climate model using the pseudo global warming approach. Using the climate response from these simulations we apply a Delta-quantile mapping technique to derive future changes in wind speed and mean sea level pressure. We apply this Delta technique to tropical cyclones Maria and Dorian’s observed wind and pressure fields to force a hydrodynamic model for simulating storm surge levels under historical and future climate conditions. Results show that the maximum storm surge heights of Maria and Dorian could increase by up to 0.31 m and 0.56 m, respectively. These results clearly show that future changes in storm surge heights are not negligible compared to end-of-the-century sea level rise projections, something that is sometimes overlooked in large-scale assessments of future coastal flood risk.

For decades, meteorologists and governments have been warning communities in coastal areas for an imminent tropical cyclone (TC) using the Saffir-Simpson Hurricane Wind Scale (SSHWS). The SSHWS categorizes a TC based on its maximum wind speed, and is used in defining evacuation strategies and humanitarian response. However, the SSHWS considers only the wind hazard of a TC, whereas a TC can also cause severe conditions through its high storm surges and extreme rainfall, triggering coastal and inland flooding. Consequently, the SSHWS fails to mirror the TC's total severity. This becomes evident when looking at past events such as Hurricane Harvey (2017), which was classified as a Tropical Storm while it caused widespread flooding in the Houston (TX) area, with precipitation totals exceeding 1.5 m. Without including storm surge and rainfall information, adequate risk communication with the SSHWS can be challenging, as the public can (mistakenly) perceive a low-category TC as a low-risk TC. To overcome this, we propose the new Tropical Cyclone Severity Scale (TCSS) that includes all three major TC hazards in its classification. The new scale preserves the categorization as used in the SSHWS, to maintain familiarity amongst the general public. In addition, we extend the scale with a Category 6, to support communication about the most extreme TCs with multiple hazards. The TCSS is designed to be applied on a local-scale, hereby supporting local-scale risk communication efforts and evacuation strategies prior to a TC landfall. The scale can be used for risk communication on both the total TC risk and on the categories of the separate hazards, which can be valuable especially in cases when one hazard is the predominant risk factor, such as excess rainfall triggering flooding.

Tropical cyclones (TC) are one of the deadliest and costliest natural disasters. To mitigate the impact of such disasters, it is essential to know extreme exceedance probabilities, also known as return periods, of TC hazards. In this paper, we demonstrate the use of the STORM dataset, containing synthetic TCs equivalent of 10,000 years under present-day climate conditions, for the calculation of TC wind speed return periods. The temporal length of the STORM dataset allows us to empirically calculate return periods up to 10,000 years without fitting an extreme value distribution. We show that fitting a distribution typically results in higher wind speeds compared to their empirically derived counterparts, especially for return periods exceeding 100-yr. By applying a parametric wind model to the TC tracks, we derive return periods at 10 km resolution in TC-prone regions. The return periods are validated against observations and previous studies, and show a good agreement. The accompanying global-scale wind speed return period dataset is publicly available and can be used for high-resolution TC risk assessments.